Re-aerosolization in liquid-based air samplers induces bias in bacterial diversity
Bibliographic record
Abstract
Bioaerosols collected in a liquid-based sampler can be re-aerosolized into the airflow during collection and lost or concentrated in the collection fluid and overestimated during the subsequent analyses. Very little information is available concerning the specific impact on bioaerosol quantification and diversity and wrong evaluation of pathogens or potentially harmful microorganisms concentration could lead to incorrect data interpretation and inaccurate exposure risk assessment. The aim of this two-part study was to better understand how evaporation impacts the results obtained from liquid-based samplers. Bacterial consortium was spiked in the collection vessels of the Coriolisµ® and the BioSampler® and the bacterial concentration was monitored after running the samplers in vitro. Relative ratios of the bacteria were analyzed using qPCR (before/after). A field study in which liquid-based air samplers in a natural environment were compared to filter-based samplers was performed. This allowed for the relative characterization of either concentration or the re-aerosolization between the two samplers using high throughput sequencing methods. Amongst the four strains of bacteria examined in vitro, results suggest differential behavior between concentration or re-aerosolization from the liquid. Re-aerosolization of bacteria is difficult to predict as the cell-surface hydrophobicity, the liquid-based air sampler and its flowrate can influence it. The sequencing results from field samples confirmed the loss of entire genera by re-aerosolization (Brevundimonas, Clostridium, Mycobacterium, and Smithella) out of the BioSampler® while concentration of several other genera were reduced (Bradyrhizobium, Delftia, Propionibacterium, and Sphingomonas). These observations suggest that evaporation in liquid samplers might lead to over- or underestimation of the prevalence of some genera.Copyright © 2019 American Association for Aerosol Research
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".